The Impact of Financial Education on Stress Among Orthopaedic Surgeons.

BACKGROUND: Financial stress has been an increasing area of concern for residents and attendings. The primary goal of this study was to determine the financial education level and differentiate financial outcome measures of orthopaedic surgery residents and attendings. METHODS: A survey of all residents and attendings of the 201 Accreditation Council for Graduate Medical Education (ACGME)-accredited orthopaedic surgery programs in the United States. RESULTS: Total participation in the study was 118 residents (postgraduate year (PGY) 1-5), three fellows (PGY 6), and 57 attending orthopaedic surgeons. A significant difference existed between average current financial stress scores between residents versus attending (2.32 vs 1.17), but not Doctor of Medicine (MD) versus Doctor of Osteopathic Medicine (DO) attendings (0.96 vs 1.67) and MD versus DO residents (2.25 vs 2.50). There was a significant difference in average future financial stress scores between residents and attendings (1.85 vs 1.44) and MD vs DO residents (1.61 vs 2.25) but no difference between MD vs DO attending (1.31 vs 1.63). Residents' confidence in financial knowledge compared to college graduates had a significantly negative Pearson coefficient with current financial stress score, while the attending group was not significant. CONCLUSIONS: Orthopaedic residents and attending physicians' financial stress levels are positively correlated with the amount of student debt they hold. Most residents who currently have no personal finance education offered in their residency would likely attend a personal finance course if offered. Decreasing the amount of debt held by residents, increasing their financial knowledge, and helping them develop good financial habits would likely lead to a decrease in financial stress.

Orthopaedic Personal Statement Thematic Review: Does Topic Matter?

Introduction: In the process of applying into medical residency, the Electronic Residency Application Service (ERAS) requires critical documents including a personal statement. Utility of personal statements are questioned based on suspected congruity of the content within personal statements among those who apply into orthopaedic surgery. The goal of this study was to identify and categorize the thematic elements found within the 2021 to 2022 personal statements of orthopaedic surgery applicants at a single institution and assess a correlation to interview invitation. Methods: Deidentified personal statements among 2021 to 2022 ERAS applicants were reviewed by the research staff and categorized into one of the proposed themes. Three hundred ninty-four applications passed initial screening filters, and 49 applicants were granted an interview. Proposed themes that were collected included: family of physician, working with hands, history of injury/disease, prior professional setting, immigration/travel, athlete/sports, reapplication, previous clinical experience, and other. χ2 test was used to analyze categorical themes and additional univariate group-to-group comparisons. Multivariate principal component analysis was performed to determine which themes were associated with interview invitation. Results: There was a significant difference in theme selection for an applicant's personal statement (χ2 = 209.5, p < 0.001), but no statistical difference was observed between each individual categorical themes and interview selection. Univariate group-to-group comparison demonstrated greater interview rates between immigration/travel compared with reapplicant and family of physician compared with working with hands. Multivariate principal component analysis demonstrated immigration/travel as the theme with a positive correlation toward an interview invitation. Conclusion: Despite a significant focus of the application process into orthopaedic surgery residencies, our single-institution study did observe specific themes that were more prevalent. There was an increased interview rate between applicant's themes for immigration/travel and family of physician when comparing groups. Immigration/travel was also identified as the only significant theme associated with interview invitation which may be due to the recent emphasis on promoting diversity within orthopaedic surgery.

Hamstring Anterior Cruciate Ligament Autograft Contributes to a Delayed Symptomatic Cyclops Lesion: A Case Report.

Cyclops lesions are characterized as fibroid nodules with granulation tissue that looks similar to a cyclops eye during arthroscopy. These are rare postoperative complications following anterior cruciate ligament reconstruction (ACLR), presenting typically within six months of their reconstruction. This case report presents a 21-year-old male, three years following hamstring autograft ACLR, with a symptomatic cyclops lesion. Contrary to the reported literature, this delayed presentation showed a painful flexion contracture of the knee and intraoperative findings consistent with a cyclops lesion. The treatment consisted of surgical debridement and notchplasty with subsequent posterior medial and lateral meniscal horn repairs. This case report presents a lesson to indicate that cyclops lesions can occur in a delayed setting following ACLR and to show a technique for successful surgical management of the lesion.

Bibliometric analysis of machine learning trends and hotspots in arthroplasty literature over 31 years.

Background: Artificial intelligence has demonstrated utility in orthopedic research. Algorithmic models derived from machine learning have demonstrated adaptive learning with predictive application towards outcomes, leading to increased traction in the literature. This study aims to identify machine learning arthroplasty research trends and anticipate emerging key terms. Methods: Published literature focused on machine learning in arthroplasty from 1992 to 2023 was selected through the Web of Science Core Collection of Clarivate Analytics. Following that, bibliometric indicators were attained and brought in to perform an additional examination using Bibliometrix and VOSviewer to identify historical and present patterns within the literature. Results: A total of 235 documents were obtained through bibliometric sourcing based on machine learning applications within the arthroplasty literature. Thirty-four countries published articles on the topic, and the United States was demonstrated to be the largest global contributor. Four hundred-five institutions internationally contributed articles, with Harvard Medical School and the University of California system as the most relevant institutes, with 75 and 44 articles produced, respectively. Kwon YM was the most productive author, while Haeberle HS and Ramkumar PN were the most impactful based on h-index. The Thematic map and Co-occurrence visualization helped identify both major and niche themes present in the scientific databases. Conclusions: Machine learning in arthroplasty research continues to gain traction with a growing annual production rate and contributions from international authors and institutions. Institutions and authors based in the United States are the leading contributors to machine learning applications within arthroplasty research. This research discerns trends that have occurred, are presently ongoing, and are emerging within this field, aiming to inform future hotspot development.

Neuroforaminal Stenosis in the Lumbosacral Spine: A Scoping Review of Pathophysiology, Clinical Manifestations, Diagnostic Imaging, and Treatment.

OBJECTIVE: To conduct the first scoping review of lumbosacral neuroforaminal stenosis with respect to the pathophysiology, symptomatic manifestations, diagnostic imaging, and treatment options. METHODS: A scoping literature review was conducted in accordance with the recommendations set forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), with English language restrictions stipulated to include articles pertaining to lumbosacral neuroforaminal stenosis. Databases maintained by PubMed, National Library of Medicine, Cochrane Central Register of Controlled Trials (Ovid), Scopus (Elsevier), Web of Science (Thomson Reuters), and Google Scholar were queried from their inception date through December 2022. SUMMARY OF THE EVIDENCE: A total of 276 articles were reviewed and 29 articles were included within the study. Within these articles, the anatomic origins of neuroforaminal stenosis were reviewed in detail and the resulting clinical manifestations were discussed. Recent studies evaluating the efficacy of existing diagnostic imaging modalities were summarized, along with potential future methods to improve sensitivity for detecting this entity and measuring foraminal stenosis via novel imaging techniques. Based on the literature, the conservative management and surgical treatment of lumbosacral foraminal stenosis were discussed. CONCLUSIONS: Lumbar neuroforaminal stenosis represents a significant source of radicular pain that is often compounded by delayed diagnosis and incomplete treatment. This article represents the first scoping review of lumbosacral neuroforaminal stenosis with focus on diagnosis, management, and treatment for associated radicular pain. The goal is to reduce the incidence of untreated or unrecognized neuroforaminal stenosis in the setting of a complex decompression and fusion, as well as to promote minimally invasive surgery to address radicular pain from neuroforaminal stenosis. Recent advances in diagnostic imaging and surgical techniques have the potential to improve the timeliness and durability of patients' treatment options. Future directions for the diagnostic imaging of foraminal stenosis include efforts aimed at developing the nascent field of computerized mapping to reliably quantify stenosis and its impact on the exiting nerve root and associated dorsal root ganglia.

The utilization of US-based large data in arthroplasty research: Bibiliometric analysis of trends and hotspots.

Background: The accessibility of digital information has expanded orthopaedic surgery with expanded role of Big Databases. The increasing interest have led to creation of large databases with increasing utilization in retrospective studies. The aim of this study is to identify Big Database research and predict future hotspots. Methods: Big Database publications between 1982 and 2022 were identified from the Web of Science Core Collection of Clarivate Analytics. Bibliometric indicators were obtained and imported for further analysis with VOSviewer and Bibliometrix to identify previous and ongoing trends within this field. Results: Bibliometric sourcing identified 811 total articles that was associated with major databases. Twenty-eight countries published manuscript in the field with the United States as the largest contributor. The most relevant institutions were Cleveland Clinic and Harvard University. Mont MA was the most productive and influential author. Co-occurrence visualization and thematic map identified niche and major themes within the literature. Conclusions: Large Database research continue to show an increasing trend since 2011 with contributions globally. United States institutions and authors are the leading contributors in big database research. This study identifies previous, current, and developing trends within this field for future hotspot development.

Improved performance of machine learning models in predicting length of stay, discharge disposition, and inpatient mortality after total knee arthroplasty using patient-specific variables.

BACKGROUND: This study aimed to compare the performance of ten predictive models using different machine learning (ML) algorithms and compare the performance of models developed using patient-specific vs. situational variables in predicting select outcomes after primary TKA. METHODS: Data from 2016 to 2017 from the National Inpatient Sample were used to identify 305,577 discharges undergoing primary TKA, which were included in the training, testing, and validation of 10 ML models. 15 predictive variables consisting of 8 patient-specific and 7 situational variables were utilized to predict length of stay (LOS), discharge disposition, and mortality. Using the best performing algorithms, models trained using either 8 patient-specific and 7 situational variables were then developed and compared. RESULTS: For models developed using all 15 variables, Linear Support Vector Machine (LSVM) was the most responsive model for predicting LOS. LSVM and XGT Boost Tree were equivalently most responsive for predicting discharge disposition. LSVM and XGT Boost Linear were equivalently most responsive for predicting mortality. Decision List, CHAID, and LSVM were the most reliable models for predicting LOS and discharge disposition, while XGT Boost Tree, Decision List, LSVM, and CHAID were most reliable for mortality. Models developed using the 8 patient-specific variables outperformed those developed using the 7 situational variables, with few exceptions. CONCLUSION: This study revealed that performance of different models varied, ranging from poor to excellent, and demonstrated that models developed using patient-specific variables were typically better predictive of quality metrics after TKA than those developed employing situational variables. LEVEL OF EVIDENCE: III.

Assessing the predictive capacity of machine learning models using patient-specific variables in determining in-hospital outcomes after THA.

Background: Machine learning is a subset of artificial intelligence using algorithmic modeling to progressively learn and create predictive models. Clinical application of machine learning can aid physicians through identification of risk factors and implications of predicted patient outcomes. Aims: The aim of this study was to compare patient-specific and situation perioperative variables through optimized machine learning models to predict postoperative outcomes. Methods: Data from 2016 to 2017 from the National Inpatient Sample was used to identify 177,442 discharges undergoing primary total hip arthroplasty, which were included in the training, testing, and validation of 10 machine learning models. 15 predictive variables consisting of 8 patient-specific and 7 situational specific variables were utilized to predict 3 outcome variables: length of stay, discharge, and mortality. The machine learning models were assessed in responsiveness via area under the curve and reliability. Results: For all outcomes, Linear Support Vector Machine had the highest responsiveness among all models when using all variables. When utilizing patient-specific variables only, responsiveness of the top 3 models ranged between 0.639 and 0.717 for length of stay, 0.703-0.786 for discharge disposition, and 0.887-0.952 for mortality. The top 3 models utilizing situational variables only produced responsiveness of 0.552-0.589 for length of stay, 0.543-0.574 for discharge disposition, and 0.469-0.536 for mortality. Conclusions: Linear Support Vector Machine was the most responsive machine learning model of the 10 algorithms trained, while decision list was most reliable. Responsiveness was observed to be consistently higher with patient-specific variables than situational variables, emphasizing the predictive capacity and value of patient-specific variables. The current practice in machine learning literature generally deploys a single model, it is suboptimal to develop optimized models for application into clinical practice. The limitation of other algorithms may prohibit potential more reliable and responsive models.Level of Evidence III.

Epidemiology of primary and revision total knee arthroplasty: analysis of demographics, comorbidities and outcomes from the national inpatient sample.

INTRODUCTION: Primary total knee arthroplasty (TKA) is a preferred treatment for end-stage knee osteoarthritis. In the setting of a failed TKA, revision total knee arthroplasty (rTKA) acts as a salvage procedure and carries a higher risk compared to primary TKA. Given increased interest in postoperative outcomes from these procedures, a thorough understanding of the demographics, comorbidities, and inpatient outcomes is warranted. This study aimed to report the epidemiological data of demographics, comorbidity profiles and outcomes of patients undergoing TKA and rTKA. METHODS: A retrospective review of NIS registry discharge data from 2006 to 2015 third quarter was performed. This study included adults aged 40 and older who underwent TKA or rTKA. A total of 5,901,057 TKA patients and 465,968 rTKA patients were included in this study. Simple descriptive statistics were used to present variables on demographics, medical comorbidities, and postoperative complications. RESULTS: A total of 5,901,057 TKA and 465,968 rTKA discharges were included in this study, with an average age of 66.30 and 66.56 years, and the major payor being Medicare, accounting for 55.34% and 59.88% of TKA and rTKA cases, respectively. Infection (24.62%) was the most frequent reason for rTKA, and was followed by mechanical complications (18.62%) and dislocation (7.67%). The most common medical comorbidities for both groups were hypertension, obesity, and diabetes. All types of inpatient complications were reported in 22.21% TKA and 28.78% of rTKA cases. Postoperative anemia was the most common complication in both groups (20.34% vs. 25.05%). CONCLUSIONS: Our data demonstrated a 41.9% increase in patients receiving TKA and 28.8% increase in rTKA from the years 2006 to 2014. The data showed a 22.21% and a 28.78% "complication" rate with TKA and rTKA, with postoperative anemia being the most common complication. The top 3 medical comorbidities were hypertension, obesity, and diabetes for both groups and with increased focus on perioperative optimization, future analyses into preoperative medical optimization, and improved primary arthroplasty protocol may result in improved postoperative outcomes.

Comorbidity, Racial, and Socioeconomic Disparities in Total Knee and Hip Arthroplasty at High Versus Low-Volume Centers.

INTRODUCTION: The purpose of this study was to compare the epidemiologic and demographic profiles and inpatient postoperative complication and economic outcomes of patients undergoing total joint arthroplasty of the hip and knee (TJA) at high-volume centers (HVCs) versus low-volume centers (LVCs) using a large national registry. METHODS: This retrospective cohort study used data from the National Inpatient Sample from 2006 to the third quarter of 2015. Discharges representing patients aged 40 years or older receiving a primary total hip arthroplasty or total knee arthroplasty were included. Patients were stratified into those undergoing their procedure at HVCs versus LVCs. Epidemiologic, demographic, and inpatient postoperative complications and economic outcomes were comparatively analyzed between groups. RESULTS: A total of 7,694,331 TJAs were conducted at HVCs while 1,044,358 were conducted at LVCs. Patients at LVCs were more likely to be female, be Hispanic, be non-Hispanic Black, and use Medicare and Medicaid than patients at HVCs. Of the 29 Elixhauser comorbidities examined, 14 were markedly higher at LVCs while 11 were markedly higher at HVCs. Patients who underwent TJA at LVCs were more likely to develop cardiac, respiratory, gastrointestinal, genitourinary, hematoma/seroma, wound dehiscence, and postoperative infection complications and were more likely to die during hospitalization. Patients at HVCs were more likely to develop postoperative anemia. Length of stay and total charges were higher at LVCs compared with HVCs. DISCUSSION: There are notable differences in the demographics, epidemiologic characteristics, and inpatient outcomes of patients undergoing TJA at HVCs versus LVCs. Attention should be directed to identifying and applying the specific resources, processes, and practices that improve outcomes at HVCs while referral practices and centralization efforts should be mindful to not worsen already existing disparities.

Epidemiology of total hip arthroplasty: demographics, comorbidities and outcomes.

BACKGROUND: Primary THA (THA) is a successful procedure for end-stage hip osteoarthritis. In the setting of a failed THA, revision total hip arthroplasty (rTHA) acts as a salvage procedure. This procedure has increased risks, including sepsis, infection, prolonged surgery time, blood loss, and increased length of stay. Increasing focus on understanding of demographics, comorbidities, and inpatient outcomes can lead to better perioperative optimization and post-operative outcomes. This epidemiological registry study aimed to compare the demographics, comorbidity profiles, and outcomes of patients undergoing THA and rTHA. METHODS: A retrospective review of discharge data reported from 2006 to the third quarter of 2015 using the National Inpatient Sample registry was performed. The study included adult patients aged 40 and older who underwent either THA or rTHA. A total of 2,838,742 THA patients and 400,974 rTHA patients were identified. RESULTS: The primary reimbursement for both THA and rTHA was dispensed by Medicare at 53.51% and 65.36% of cases respectively. Complications arose in 27.32% of THA and 39.46% of rTHA cases. Postoperative anemia was the most common complication in groups (25.20% and 35.69%). Common comorbidities in both groups were hypertension and chronic pulmonary disease. rTHA indications included dislocation/instability (21.85%) followed by mechanical loosening (19.74%), other mechanical complications (17.38%), and infection (15.10%). CONCLUSION: Our data demonstrated a 69.50% increase in patients receiving THA and a 28.50% increase in rTHA from the years 2006 to 2014. The data demonstrated 27.32% and 39.46% complication rate with THA and rTHA, with postoperative anemia as the most common cause. Common comorbidities were hypertension and chronic pulmonary disease. Future analyses into preoperative optimizations, such as prior consultation with medical specialists or improved primary hip protocol, should be considered to prevent/reduce postoperative complications amongst a progressive expansion in patients receiving both THA and rTHA.

Magnetic resonance imaging of the quadriceps tendon autograft in anterior cruciate ligament reconstruction.

BACKGROUND: Quadriceps tendon (QT) autograft is emerging as a popular technique for primary anterior cruciate ligament (ACL) reconstruction. Studies have shown that it has comparable outcomes to bone-patellar tendon-bone (BPTB) and hamstring tendon (HT) autografts while mitigating post-operative complications associated with these grafts. PURPOSE: To provide a literature summary of the important pre- and post-operative magnetic resonance imaging (MRI) findings of the quadriceps tendon and pertinent postoperative complications associated with the QT harvest. Radiologists should be familiar with MR findings after autologous graft harvest of the quadriceps tendon for reconstruction of the ACL. LEVEL OF EVIDENCE: Level IV.